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AGENTS.md

This file provides guidance to Claude Code (claude.ai/code) / Codex when working with code in this repository.

Project Overview

StructureClaw is an AI-assisted structural engineering workspace. It takes natural-language structural descriptions through a conversational UI and runs them through a pipeline: draft model → validate → analyze → code-check → report. The system is bilingual (en / zh); all new user-visible text must ship in both locales.

Architecture

frontend (Next.js 14)  →  backend (Fastify + Prisma)  →  analysis engine (e.g. Python analysis runtime (OpenSees))
  • Backend (backend/): Fastify API with Prisma ORM (SQLite default). Route handlers in src/api, orchestration/domain logic in src/services, shared helpers in src/utils.
  • Frontend (frontend/): Next.js 14 app-router. Routes in src/app, reusable UI in src/components, client state (Zustand) and i18n in src/lib.
  • Agent runtime (backend/src/agent-runtime/): Capability-driven orchestration — base model (chat) → skill layer (engineering advisor) → tool layer (executable agent). 14 skill domains live under backend/src/agent-skills/ (structure-type, analysis, code-check, data-input, design, drawing, general, load-boundary, material, report-export, result-postprocess, section, validation, visualization).
  • Analysis execution (backend/src/agent-skills/analysis/): Spawns a Python subprocess using OpenSees for structural analysis. Python runtime lives in backend/.venv/.
  • CLI (scripts/cli/): The ./sclaw and ./sclaw_cn commands wrap all startup, health-check, and lifecycle operations. Always prefer ./sclaw ... over calling script paths directly.
  • Tests (tests/): Regression and contract validation via node tests/runner.mjs.

Build & Run

Environment setup (first time):

./sclaw doctor          # checks deps, installs missing, sets up backend/.venv
./sclaw start           # starts backend + frontend
./sclaw status          # verify both services running
./sclaw logs            # tail logs
./sclaw stop            # graceful shutdown

China mirror variant: Replace ./sclaw with ./sclaw_cn for mirror defaults (npmmirror, tsinghua PyPI, Docker mirror).

Backend only:

npm run build --prefix backend          # TypeScript compile
npm run lint --prefix backend           # ESLint
npm test --prefix backend -- --runInBand  # Jest (internally runs db:generate + build first)
npm run db:generate --prefix backend    # Regenerate Prisma client (required before build after schema changes)
npm run db:push --prefix backend        # Sync Prisma schema to SQLite (no migration files)

Frontend only:

npm run build --prefix frontend         # Next.js production build
npm run type-check --prefix frontend    # tsc --noEmit
npm run test:run --prefix frontend      # Vitest (all tests)
npm run lint --prefix frontend          # ESLint
npm run test:e2e --prefix frontend      # Playwright E2E tests

Running a single test:

# Backend (Jest)
npm test --prefix backend -- --runInBand path/to/test.test.ts

# Frontend (Vitest)
npm run test:run --prefix frontend -- path/to/test.test.ts

Regression and validation:

node tests/runner.mjs analysis-regression                    # OpenSees analysis regression
node tests/runner.mjs check backend-regression               # Backend regression suite (build + lint + Jest + validations)
node tests/runner.mjs validate validate-agent-orchestration  # Agent orchestration contract
node tests/runner.mjs validate validate-chat-stream-contract # Chat stream contract
node tests/runner.mjs validate validate-analyze-contract     # Analyze endpoint contract
node tests/runner.mjs smoke-native                           # Full native install smoke (mirrors CI)
node tests/runner.mjs smoke-docker                           # Docker smoke (needs Docker)
node tests/runner.mjs llm-integration                        # LLM integration tests (needs LLM_API_KEY)

Key Conventions

  • TypeScript: strict mode, ES modules, 2-space indent, semicolons. Explicit types at API/store boundaries. Route handlers stay thin; logic goes into services.
  • Naming: files = lowercase domain (agent.ts, analysis.ts), classes = PascalCase, functions/variables = camelCase, constants = UPPER_SNAKE_CASE.
  • Bilingual: Every user-visible string must support en and zh. Route through the existing i18n system; never hardcode single-language strings.
  • Frontend: Preserve Next.js app-router structure. Use existing Zustand store, i18n, and theme infrastructure. Prefer design-token styling over hardcoded light/dark values.
  • Python: Follow existing style in analysis modules. Keep regression fixtures deterministic.
  • Immutability: Prefer creating new objects over mutating existing ones.
  • Commits: Conventional commit format (feat(frontend): ..., fix(backend): ..., docs: ...). Small logical commits with clear boundaries; don't bundle unrelated work.

Module Boundaries

Concern Location
API routes backend/src/api/
Agent orchestration backend/src/services/ and backend/src/agent-runtime/
Skill domains backend/src/agent-skills/<domain>/
Executable agent tools backend/src/agent-tools/<tool>/ (run_analysis, validate_model, etc.)
Shared skill utilities backend/src/skill-shared/
Shared helpers backend/src/utils/
Frontend pages frontend/src/app/
Reusable components frontend/src/components/
Client state / i18n frontend/src/lib/
CLI implementation scripts/cli/

Configuration

Environment variables live in .env (template: .env.example). Key variables:

  • LLM_API_KEY, LLM_MODEL, LLM_BASE_URL
  • DATABASE_URL — SQLite by default (file:../../.runtime/data/structureclaw.db)
  • ANALYSIS_PYTHON_BIN — leave blank for auto-detected backend/.venv Python

Testing Guidance

  • Backend: cover success, failure, and missing-input scenarios. Run npm test --prefix backend -- --runInBand.
  • Frontend: run targeted Vitest checks plus type-check. Run build when layout/routing changes.
  • Both en and zh paths must be verified for new user-visible frontend features.
  • Analysis runtime: keep regression fixtures deterministic; don't casually change expected outputs.
  • If changes affect chat, agent orchestration, report output, converters, or schema — run node tests/runner.mjs validate <name>.

Agent Architecture Reference

The agent is capability-driven, not mode-driven:

  1. Base model (always present): general dialogue, fallback when no engineering capabilities are loaded.
  2. Skill layer (optional): engineering intent classification, parameter extraction, guidance.
  3. Tool layer (optional): executable engineering actions (analyze, validate, code-check, report).

See docs/agent-architecture.md for the full canonical design reference.

Security and Config

  • Never commit live secrets, tokens, or private keys.
  • Use .env.example as the template.
  • Backend runtime depends on environment configuration for the OpenAI-compatible LLM interface and infrastructure; document any new defaults or required variables.
  • When documenting LLM setup, prefer the existing OpenAI-compatible LLM_API_KEY + LLM_BASE_URL + LLM_MODEL pattern.

Commit and PR Guidance

  • Follow conventional commit style, for example:
    • feat(frontend): add bilingual light and dark experience
    • fix(frontend): stop chat auto-scroll from locking wheel
    • docs: map existing codebase
  • Commit in small batches with clean boundaries and do not postpone commits once a logical slice is complete.
  • Preferred sequence when applicable:
    • implementation changes first
    • tests in a separate commit when that improves reviewability
    • docs or workflow-note follow-ups in their own commit
  • PRs should state:
    • what changed and why
    • impacted areas (backend, frontend, scripts, docs)
    • commands run and results
    • sample request/response when API behavior changed

Skill Development

A skill is the unit of extensibility in the agent. Each skill lives under backend/src/agent-skills/<domain>/ and consists of two parts:

1. Skill Manifest (skill.yaml)

Required file. Declares identity, capabilities, and routing. Schema validated by backend/src/agent-runtime/manifest-schema.ts. Key fields:

id: frame                          # unique skill identifier
domain: structure-type              # one of 14 domains (see ALL_SKILL_DOMAINS)
name: { zh: 规则框架, en: Regular Frame }
description: { zh: ..., en: ... }
triggers: [frame, 框架, steel frame] # keywords for intent detection
stages: [intent, draft, analysis, design]
structureType: frame               # maps to InferredModelType
autoLoadByDefault: true
priority: 70                       # higher = preferred when multiple skills match
compatibility:
  minRuntimeVersion: "0.1.0"
  skillApiVersion: v1

2. Stage Markdown (<stage>.md, at least one required)

Markdown files (intent.md, draft.md, analysis.md, design.md) that provide domain knowledge prompts injected into the LLM context at each pipeline stage. Content-only; no YAML frontmatter. At least one stage .md file is required for the AgentSkillLoader to discover the skill directory.

3. Handler Module (handler.ts, optional but required for interactive skills)

Exports a SkillHandler object (interface defined in backend/src/agent-runtime/types.ts). The handler methods form the skill's lifecycle:

detectStructuralType()  → match user message to this skill's structural type
parseProvidedValues()   → normalize explicit user input into DraftExtraction
extractDraft()          → combine LLM extraction + rules into DraftExtraction
mergeState()            → immutable merge of existing state + new patch
computeMissing()        → return critical/optional missing fields
mapLabels()             → localize missing-field keys for display
buildQuestions()        → generate InteractionQuestion[] for missing fields
buildDefaultProposals() → suggest default values (optional)
buildModel()            → produce the final computable model JSON
resolveStage()          → determine pipeline stage from current state (optional)
buildReportNarrative()  → custom report narrative (optional)

Creating a New Skill

  1. Create directory: backend/src/agent-skills/<domain>/<skill-name>/
  2. Add skill.yaml with all required fields (validate against skillManifestFileSchema)
  3. Add at least one stage markdown file (intent.md, draft.md, etc.) — required for auto-discovery
  4. For structure-type skills with interactive drafts, add handler.ts implementing SkillHandler
  5. Add tests in <skill-dir>/__tests__/

Skills are auto-discovered by the AgentSkillLoader — no manual registration is needed.

Skill Loading Flow

  1. AgentSkillLoader scans agent-skills/ recursively for skill.yaml files
  2. Manifests validated via Zod (skillManifestFileSchema)
  3. Stage markdown loaded and bundled per skill ID
  4. Handler modules dynamically imported from handler.ts/handler.js
  5. AgentSkillRegistry resolves plugins by skillId or structureType at runtime
  6. AgentSkillRuntime orchestrates: detect type → extract params → build model → execute tools

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing:

  • State your assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them - don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No "flexibility" or "configurability" that wasn't requested.
  • No error handling for impossible scenarios.
  • If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

  • Don't "improve" adjacent code, comments, or formatting.
  • Don't refactor things that aren't broken.
  • Match existing style, even if you'd do it differently.
  • If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:

  • Remove imports/variables/functions that YOUR changes made unused.
  • Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

  • "Add validation" → "Write tests for invalid inputs, then make them pass"
  • "Fix the bug" → "Write a test that reproduces it, then make it pass"
  • "Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

These guidelines are working if: fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clarifying questions come before implementation rather than after mistakes.